1. Field of the Invention
The present invention relates generally to equipment and process monitoring, and more particularly to monitoring systems instrumented with sensors that measure correlated phenomena. The present invention further relates to modeling instrumented, real-time processes using the aggregate sensor information to ascertain information about the state of the process.
2. Description of the Related Art
Conventional methods are known for monitoring equipment or processes xe2x80x94generically xe2x80x9csystemsxe2x80x9dxe2x80x94using sensors to measure operational parameters of the system. The data values from sensors can be observed directly to understand how the system is functioning. Alternatively, for unattended operation, it is known to compare sensor data values against stored or predetermined thresholds in an automated fashion, and generate an exception condition or alarm requiring human intervention only when a sensor datum value exceeds a corresponding threshold.
A number of problems exist with monitoring systems using thresholds. One problem is the difficulty of selecting a threshold for a dynamic parameter that avoids a burdensome number of false alarms, yet catches real alarms and provides sufficient warning to take corrective action when a system parameterxe2x80x94as measured by a sensorxe2x80x94moves outside of acceptable operation. Another problem is posed by sensor failure, which may result in spurious parameter values. It may not be clear from a sensor data value that the sensor has failed. Such a failure can entirely undermine monitoring of the subject system.
In systems with a plurality of sensors measuring correlated phenomena in the system, it is known to use certain methods to consider all sensors in aggregate to overcome some of these problems. By observing the behavior of all the sensor data values in aggregate, it can be possible to dramatically improve monitoring without suffering unduly from false and missed alarms. Also, knowledge of how all the correlated parameters behave in unison can help determine that a sensor has failed, when isolated monitoring of data from that sensor in and of itself would not indicate the sensor failure.
Known methods for viewing aggregate sensor data typically employ a modeling function that embodies prior knowledge of the system. One such technique known as xe2x80x9cfirst-principlesxe2x80x9d modeling requires a well-defined mathematical description of the dynamics of the system, which is used as a reference against which current aggregate sensor data can be compared to view nascent problems or sensor failures. However, this technique is particularly vulnerable to even the slightest structural change in the observed system. The mathematical model of the system is often very costly to obtain, and in many cases, may not be reasonably possible at all.
Another class of techniques involves empirically modeling the system as a xe2x80x9cblack boxxe2x80x9d without discerning any specific mechanics within the system. System modeling using such techniques can be easier and more resilient in the face of structural system changes. Modeling in these techniques typically involves providing some historic sensor data corresponding to desired or normal system operation, which is then used to xe2x80x9ctrainxe2x80x9d the model.
One particular technique is described in U.S. Pat. No. 5,987,399, the teachings of which are incorporated herein by reference. As taught therein, sensor data is gathered from a plurality of sensors measuring correlated parameters of a system in a desired operating state. This historical data is used to derive an empirical model comprising certain acceptable system states. Real-time sensor data from the system is provided to a modeling engine embodying the empirical model, which computes a measure of the similarity of the real-time state to all prior known acceptable states in the model. From that measure of similarity, an estimate is generated for expected sensor data values. The real-time sensor data and the estimated expected sensor data are compared, and if there is a discrepancy, corrective action can be taken.
The bounded area ratio test (BART) as taught in U.S. Pat. No. 5,987,399, is a well known state of the art similarity operator, wherein an angle is used to gauge the similarity of two values. The similarity operator is insensitive to variations across the training set range of the particular signal or sensor. BART uses the sensor range of values from low to high across all snapshots in the training set to form the hypotenuse of a trianglexe2x80x94preferably a right trianglexe2x80x94which is its base. BART, therefore, forms a straight line with minimum and maximum expected values disposed at either end. During system monitoring, BART periodically maps two points representative of an expected and a parameter value onto the base. These two points are placed, according to their values, within the range of values in the training set. A comparison angle is formed at the apex, opposite the base, by drawing a line to the apex from each of the points and the angle is the basis by which two values are compared for similarity. Furthermore, BART typically locates the apex point at a point above the median or mean of the range, and at a height that provides a right angle at the apex (for easy computation).
BART does not exhibit equal sensitivity to similarity values across the base range. Differences between values in the middle of the range, i.e., around 45∘ are amplified, and differences at the ends of the range, i.e., at 0∘ or 90∘ are diminished. Consequently, prior models, such as those employing a BART operator or other operators, might not optimally model all non-linear systems. In certain value ranges for certain sensors, these prior models may be inaccurate. Apart from selecting new or additional training data, both of which require additional time, as well as computer capacity, without providing any guarantee of improving the model, no effective way has been found in the prior art to adjust the empirical model to improve modeling fidelity.
Thus, there is a need for system monitoring mathematical operators for accurately measuring similarities between a monitored system and expected system states, flexibly modeling and improving model sensitivity such that component failures can be accurately predicted and so that acceptably functioning components are not prematurely replaced.
It is an object of the present invention to provide for equipment and process monitoring using empirical modeling with a class of improved operators for determining measures of similarities between modeled or known states of a system and a current or selected state of the system.
The present invention provides for monitoring equipment, processes or other closed systems instrumented with sensors and periodically, aperiodically or randomly recording a system snapshot therefrom. Thus, a monitored system, e.g., equipment, a process or any closed system, is empirically modeled using improved operators for determining system state similarity to known acceptable states. The improved operators provide for modeling with heightened or adjusted sensitivity to system state similarity for particular ranges of sensor values. The invention thus provides for greater possible fidelity of the model to the underlying monitored system.
The similarity between a system data snapshot and a selected known state vector is measured based on similarity values between corresponding parameter values from the data snapshot and the selected known state vector. Each similarity value is effectively computed according to a ratio of angles formed by the difference of the corresponding data values and by the range of corresponding values across all the known state vectors. Importantly, the ratio of angles is affected by the location within this range of the data value from the snapshot and the data value from the selected known state vector. The similarity engine can be flexibly honed to focus as through a lens on certain parts of the range with altered sensitivity, expanding or contracting those parts.
The similarity operator class of this invention can be used in a multivariate state estimation technique (MSET) type process monitoring technique as taught in U.S. Pat. No. 5,764,509, and can also be used for a variety of complex signal decomposition applications. In these applications, a complex signal can be decomposed into components (e.g., a frequency domain or wavelets), which are input to this MSET similarity engine. The similarity operator can be embodied both as general purpose computer software for a mainframe computer or a microprocessor or as code for an embedded processor. The result of the similarity operation can be used for generating estimated or expected states, or for identifying which one of a finite set of patterns stored in memory that most closely matches the input pattern.
By allowing selection of a curve instead of the base of a triangle in combination with angle selection, the present invention adds the advantage of providing a lens function for xe2x80x9clensingxe2x80x9d certain parts of the range for greater or lesser sensitivity to differences that, ultimately, are reflected in the similarity for the two values. Where ease of computation is not an issue, the present invention provides improved lensing flexibility that allows freeform location of the apex point at different locations above the base.
The advantage afforded by lensing is that focus can be directed to different regions of interest in a particular range for a given sensor, when performing a similarity determination between a current state vector and a prior known expected state vector. Using this similarity determination an estimated state vector can be computed for a real-time system that is being monitored and modeled using MSET or the like. The model performance can be honed for improved model estimates using the improved class of similarity operators of the present invention.
The similarity operation of the present invention is rendered particularly non-linear and adaptive. The present invention can be used in system state classification, system state alarm notification, system virtual parameter generation, system component end of life determination and other techniques where an empirical model is useful. The present invention overcomes the above restrictions of the prior art methods by providing more flexibility to adapt and improve modeling fidelity.
The present invention also includes a similarity engine in an information processor embodiment. Preprocessed known state vectors characteristic of a desired operating condition, i.e., historic data, of a monitored system are stored in memory. A data acquisition unit acquires system parameter data, such as real-time sensor data, representative of the current state of the monitored system. The information processor is coupled to the memory and to the data acquisition system, and operates to process one system state frame or snapshot at a time from the data acquisition unit against the known state vector snapshots in the memory. A measure of similarity is computed between system state snapshots from the data acquisition unit and each known state vector in the memory. An expected state vector is computed from the snapshot for the monitored system.
The information processor may be further disposed to compare the state snapshots with the expected state vectors sequentially, to determine if they are the same or different. This determination can be used for an alarm or event trigger.
Briefly summarized, in a machine for monitoring an instrumented process or for analyzing one or more signals, an empirical modeling module for modeling non-linearly and linearly correlated signal inputs using a non-linear angular similarity function with variable sensitivity across the range of a signal input is described. Different angle-based similarity functions can be chosen for different inputs to improve sensitivity particular to the behavior of that input. Sections of interest within a range of a signal input can be lensed for particular sensitivity.